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A chatbot is the most in-your-face use case of AI, but it’s easy to underestimate the opportunities that AI can help us realize. By some estimates, by 2023 around 40% of all internal operations teams in Enterprises will be AI-enabled. The flip side is that even though the growth opportunities are huge, it will take time, effort, and a concerted strategy to realize the true potential.

Let us look at the key considerations to factor in while embarking on the AI journey.

Definite Use Cases:

It is imperative to have a definite use case in mind before one thinks of implementing AI in your Enterprise. Many implementations fail simply because they are implemented with no thought about the end goal to be achieved. To avail a great ROI, it is extremely important that one has a clear definition of the specific business goals to shoot for. For instance, a customer service operation may want to reduce the number of customer service calls by a factor of 50%. Chatbot-enabled engines could help -and after a defined period you can establish clearly if the initiative was a success.

Think Big Start Small:

It is best to have lofty goals while aiming for a transformation with AI but start with a small test or a pilot project. It’s always prudent to test the waters before taking the plunge. Chose one particular LOB, or a small department to test AI and its viability for this particular endeavor. This will throw up the problems one can encounter while undergoing a transformation. And at the same time, you will also identify the challenges resident within the ecosystem that may have to be addressed for achieving a seamless transformation.

Creation of a Knowledge Repository:

The success of an AI implementation is dependent on how robust the underlying knowledge base is. This requires data, lots of it. The AI will learn as it goes along -but even at the stage of training the AI, vast amounts of data is needed. The idea is to have the AI system define how a problem can be solved and be driven by the relevant insights the AI provides. By having a highly mature algorithm driven by a robust database you can improve the quality of the insights available. The primary difference between a normal knowledge repository and a Knowledge repository for AI is in the structure and the content. For AI, an interface along with highly structured data which can be queried is necessary.

Build or Buy and choosing the Correct Partner:

AI may be necessary for every organization but not every organization will have the requisite resources to implement it on their own. You could build the expertise, or you may have to work with a partner.Picking the right partner is a crucial decision. The selection should be driven by considerations like the availability of skilled human resources, successful past implementation,understanding of your business challenges, and their future roadmap.

Data Quality:

For AI data quantity is not enough, data quality is paramount. AI is driven by Data Science and statistical algorithms. These algorithms become trustworthy if the data quality of the data set on which the system is being trained and implemented is pure and pristine. That is the reason why there should be a state-of-the-art data quality monitoring system. You may have to fix the data duplication issues and weed out the corrupt and broken data.

Cloud or On-Premise:

Once put into place, the knowledge repository will increase in size at an exponential rate. A tsunami of streaming data will fill up the data storage really fast. Hence many organizations consider the cloud for storing the data. The answer to the question of whether to go for cloud or stay on-premise will be driven by factors like the security and compliance requirements, apart from the cost and storage volume needed.

Right Resource Pool:

Irrespective of the decision to build or buy it’s true that there are not many trained and experienced human resources out there. It is common to underestimate the demands AI will make on the business. This is not just about the technical resources needed to implement the systems. AI strategies sometimes fall apart because the Enterprise didn’t train or develop their functional resources to cater to the new ways of working. Business processes will change, agility will increase, and responsibilities will shift -your people will have to be ready.

Top Management Buy-in:

Like any other strategic initiative, the involvement of the top management is a key factor for the success of any AI implementation. Many Enterprises still work top-down. With top management throwing its weight behind a project, the probability of its success increases exponentially. The organization starts treating the implementation with the required seriousness. Resources get allocated, Results get tracked.

Conclusion:

As you can see, there are quite a few factors to bake into the implementation of your Enterprise AI initiative. Knowing these factors and staying hyper-focused will help you stay on track with your AI initiative. And implementing a robust AI strategy that has the greatest chance of delivering business impact is what it’s all about -isn’t it?

We have written a couple of times in the past about Microservices. The approaches are evolving, and this blog is an attempt to address a specific question -while testing microservices, does test automation have a role?

Just a little refresher first. As the name suggests, microservices are nothing but a combination of multiple small services that make up a whole. It is a unique method of developing software systems that focus on creating single-function modules with well-defined interfaces and operations. An application built as microservices can be broken down into multiple component services. Each of these services can be deployed, modified, and then redeployed individually without compromising the integrity of an application. This enables you to change one or more distinct services (as and when required) instead of having to redeploy the application as a whole.

Microservices are also highly intelligent. They receive requests, process them, and produce a response accordingly. They have smart points that process information and apply logic, and then direct the flow of the information.

Microservices architecture is ideal in the case of evolutionary systems, for eg. where it is not possible to thoroughly anticipate the types of devices that may be accessing the application in the future. Many software products start based on a monolithic architecture but can be gradually revamped to microservices as and when unforeseen requirements surface that interact over an older unified architecture through APIs.

Why is Testing for Microservices Complicated?

In the traditional approach to testing, every bit of code needs to be tested individually using unit tests. As parts are consolidated together, they should be tested with integration testing. Once all these tests pass, a release candidate is created. This, in turn, is put through system testing, regression testing, and user-acceptance testing. If all is well, QA will sign-off, and the release will roll out. This might be accelerated while developing in Agile, but the underlying principle would hold.

This approach does not work for testing microservices. This is mainly because apps built on microservices use multiple services. All these services may not be available on staging at the same time or in the same form as they are during production. Secondly, microservices scale up and share the demand. Therefore, testing microservices using traditional approaches can be difficult. In that scenario, an effective way to conduct microservices testing is to leverage test automation.

Quick Tips on How to Automate Testing for Microservices:

Here are some quick tips that will help you while testing your microservices-based application using test automation.

Manage each service as a software module.

List the essential links in your architecture and test them

Do not attempt to gather the entire microservices environment in a small test setup.

Test across different setups.

How to Conduct Test Automation for Microservices?

Each Service Should Be Tested Individually:Test automation can be a powerful mechanism for testing microservices. It is relatively easy to create a simple test script that regularly calls the service and matches a known set of inputs against a proposed output. This function by itself will free up your testing team’s time and allow them to concentrate on testing that is more complex.

Test the Different Functionalities of your Microservices-based Application: Once the vital functional elements of the microservices-based application have been identified, they should be tested much like you would conduct integration testing in the traditional approach. In this case, the benefits of test automation are obvious. You can quickly generate test scripts that are run each time one of the microservices is updated. By analyzing and comparing the outputs of the new code with the previous one, you can establish if anything has changed or has broken.

Refrain from Testing in a Small Setup:Instead of conducted testing in small local environments, consider leveraging cloud-based testing. This allows you to dynamically allocate resources as your tests need them and freeing them up when your tests have completed.

Test Across Diverse Setups: While testing microservices, use multiple environments to test your code. The reason behind this is to expose your code to even slight variations in parameters like underlying hardware, library versions, etc. that might affect it when you deploy to production.

Microservices architecture is a powerful idea that offers several benefits for designing and implementing enterprise applications. This is why it is being adopted by several leading software development organizations. A few examples of inspirational software teams leveraging microservices include Netflix, Amazon, eBay, etc. If like these software teams, your product development is also adopting microservices then testing would undoubtedly be in focus. As we have seen, testing these applications is a complex task and traditional methods will not do the job. To thoroughly test an application built on this model, it may be essential to adopt test automation. Would you agree?

Artificial Intelligence, more popularly known as AI, might no longer be the new technology on the block, but it is ‘the’ technology that everyone is talking about. Facial recognition, digital assistants, autopilots etc. are examples of the existing AI around us. AI is emerging as that disruptive technology that will change the way we live and work. While AI has been seen often in a consumer-centric world, the enterprise too is warming up to this technology.

2018 witnessed widespread adoption of AI in different industries as organizations realized the value AI brought to the table – be it in improving operations, assisting the data analytics drive, boosting innovation, and improving customer experience amongst other things. Owing to the immense value AI brings to the table, the global AI market size is expected to reach $169,411.8 million in 2025, from $4,065 million in 2016 growing at a CAGR of 55.6% from 2018 to 2025 according to MarketWatch.

So, what transformative value does AI bring for the enterprise? Here’s a look at how AI will transform enterprises and change the future of work.

The New age of Automation:AI is going to give automation the boost that it needs. As enterprises look towards technologies such as Robotic Process Automation (RPA), with AI we shall be moving into the world of Intelligent Process Automation. IPA combines process automation with Robotic Process Automation (RPA) and Machine learning (ML) and creates choreographic connections between people, processes, and systems. IPA will not only automate structured tasks but also generate intelligence from process execution.

IPA is all set to increase the level of transparency in business processes, optimizing back-office operations, increasing process efficiency and customer experience, and improving workforce productivity considerably. Along with this, IPA also holds the promise of reducing costs and risks and promises more effective fraud detection. Owing to these benefits, the IPA market is expected to be worth $13.75 billion by 2023.

The Rise and Rise of Chatbots:The friendly chatbot has already made some inroads into the enterprise. With AI, the chatbot invasion is going to become more pervasive in the enterprise of the future. Customer-facing industries such as retail, healthcare, banking, and financial services shall witness the rise of AI-powered voice assistants such as Alexa or Sirito create interactive experiences for the customer without pushing the load of delivering exceptional customer experiences on the staff alone. Chatbots will also become the norm to service the internal customers of the organizations, the employees. Enterprise chatbots will be powered by AI technologies such as NLP (Natural Language Processing), semantic search, and voice recognition. They will enhance search capabilities and deliver a new way for employees to interact with corporate data to improve their productivity.

AI and the UX Impact: The focus on User Experience or UX is only going to keep increasing. With AI, the user experience will not be driven by guesswork but by faster analysis of the right data, by the enterprises in the future. User experiences with software products, even within the enterprise, have to mimic consumer-grade experiences.

Fluid, intuitive, efficient, and highly-personalized user experiences are going to be the norm. UX is also going to be the defining factor in product success and acceptance. Enterprises will look at the insights provided by AI by intelligent information gathering and identifying patterns to deliver greater value to the end-user. This will make the user experience of products highly intuitive and intelligent as well.

Greater Intelligent Customization Capabilities: As we move deeper into the age of personalization, enterprises will have to look towards technologies such as AI to develop intelligent customization capabilities. Data is already improving the customization capabilities of enterprises.

With cognitive technologies such as AI, they will be able to further improve their customization capabilities and create products that individual users will love. Leveraging user data and faster data-processing capabilities, AI can speed up interactions and provide intelligent insights to develop products and solutions that can be highly customized to meet user demands.

Cutting Edge Analysis To Bolster Data-Driven Decisions: AI will be leveraged in the enterprise to perform advanced data investigation in less time to improve business process, product, and service efficiencies. AI technologies have the capability to analyze usage patterns and then deliver deep insights that will take data-driven decision making to the next level.

Whether it is for predictive maintenance or predictive analytics for product development, or risk management or planning, the AI impact will make the enterprise smarter and more proactive in its decision-making.

AI In Software Development and Testing: Software Development and Testing will also feel the AI impact as this technology gets more pervasive. To respond to the market need for robust, reliable, and high-quality software that is delivered faster, AI technologies will get ingrained into the development and testing lifecycle.

With self-learning algorithms that are designed to self-improve, enterprises will be looking at improving the efficiency of the process of software development. They will leverage automated code-generation, among other things, and achieve a shorter time to market with greater confidence.

While AI has met with a certain resistance in the past, the coming years will see this technology achieve greater maturity. Given the immense value that AI can deliver, it is only a matter of time before AI will become a necessity for the enterprises that wish to remain relevant in this ever-evolving and competitive marketplace.

Technology has created our software-defined world of today. As technologies change and evolve, we see the rise of new software development trends to further augment this growth. 2018 was no exception. We saw some exciting developments in the world of software development. We witnessed the rise of cloud-based software development, the cementing of DevOps, and the ever-growing importance of testing. But what about the year ahead? Here’s a look at some of the trends that will impact software development in 2019.

Artificial Intelligence (AI):

Gartner estimates that the revenue from the AI industry will touch $1.2 trillion by the end of 2019. By 2022 the business value derived from AI is expected to reach $3.9 trillion. With the digital transformation wave taking over almost all organizations, it is clear that AI will continue to trend in the software universe for the next couple of years.

In 2019 we will see the use of AI to speed up and improve the accuracy of software development- be it in automatic debugging, for creating intelligent assistants to speed up development processes, to automate code generation, or to create and train an automated system to produce accurate estimates to develop MVPs faster…the AI impact will be all around and hard to ignore.

Blockchain:

Blockchain, the meta-technology, holds the promise of completely reshaping software development. Blockchain consists of a single ledger of transactions and enables smart contracts. It has a distributed database that is accessible to a peer-to-peer network but is protected against unauthorized access. The technology is secured by cryptographic technique making the applications developed using this tech more secure.

Blockchain has already made its presence felt in several different sectors be it retail, banking, financial services, healthcare, and public administration. It is only a matter of time before Blockchain becomes a prime focus for organizations involved in software development. As security becomes top of the mind, the need for blockchain-based applications will increase. 2019 looks like a good year to jump on this technology trend.

Progressive Web Apps:

Progressive Web Apps leaped to our attention when Gartner announced it as a software trend in 2017. In 2019 however, with a more mature app ecosystem in place, we expect to see Progressive Web apps become more dominant and gain that promised place. As the app economy gets stronger and the mobile environment evolves, progressive web apps are gradually going to become the new normal in this changing environment.Research shows that progressive web apps show a 68% increase in mobile traffic and are 15 times faster to load and install as compared to native apps. Progressive web apps also require 25 times less device storage space in comparison to native apps. These applications are also less complex to develop, are easy to maintain and provide the benefits of mobile experience with the features of browser technology. What’s not to love?

Security Rules:

Security has been on everyone’s mind in the software development space. The increased focus of security during software development is only going to increase in this year. Research from Alert Logic showed that data loss and leakage is one of the biggest concerns for cybersecurity professionals (67%). Threats to data privacy were the concern for 61% while 53% were concerned with breach of confidentiality.

Owing to the huge impact security issues can have on a software product and its users, organizations are conscious of baking security into the process of software development. Software development companies also have to keep a close eye on regulatory considerations for specific industries.

They must also follow security best practices and ensure that all security guidelines and protocols are met consistently. To make security more robust, organizations are also looking at Managed Security Providers or MSP’s for robust application security without compromising on development timelines.

Automated Testing:

While test automation has been around for a while, automated testing will continue to be a trend in 2019. This will continue for as long as there is a need to release better-tested products into the market faster -that means, forever! As testing gets deeply ingrained into every software development methodology, test automation will get even more pervasive with testing teams striving for greater levels of automated test coverage.

In 2019 we will witness test automation leveraging AI for better test accuracy. Tests will become more comprehensive, more intelligent, and more dependable. Even with that, that they will become faster and less taxing. The products will become better tested and more robust as a result.

Borrowing from, and evolving the technologies that help to automate testing, Robotic Process Automation or RPA will also become a dominating trend in 2019. RPA will drive to automate high-volume repeatable tasks, thus making them faster, more accurate, and less effort-intensive.

Conclusion:

2019 promises to be an exciting year in the world of software development. It will be interesting to see how these trends develop over the course of the year. Check back with us at the end of the year for a review of our predictions. And, do feel free to add more about the trends you think will dominate software development and testing in 2019.

According to Gartner, by 2020, AI technologies will be pervasive in almost every new product and service and will also be a top investment priority for CIO’s. 2018 really was all about Artificial Intelligence. Tech giants such as Microsoft, Facebook, Google, Amazon and the like spent billions on their AI initiatives. We started noticing the rise of AI as an enterprise technology. It’s now clear how AI brings new intelligence to everything it touches by exploiting the vast sea of data at hand. Influential voices also started talking about the paradigm shift that this technology would bring to the world of software development. Of course, software testing too has not remained immune to the charms of AI.

Role: AI In Software Testing.

But first, Why do we Need AI for Software Testing?

It seems like we have only just firmly established the role of test automation in the software testing landscape and we must start preparing for further disruptions promised by AI! The rise of test automation was driven by development methodologies such as Agile and the need to ship bug and error-free, robust software products into the market faster. From there we have progressed into the era of daily deployments with the rise of DevOps. DevOps is pushing organizations to accelerate the QA cycle even further, to reduce test overheads, and to enable superior governance. Automating test requirement traceability and versioning are also factors that now need careful consideration in this new development environment.

The “surface area” of testing has also increased considerably. As applications interact with one another through API’s leveraging legacy systems, the complexity tends to increase as the code suites keep growing. As the software economy grows and enterprises push towards digital transformation, businesses now demand real-time risk assessment across the different stages of the software delivery cycle.

The use of AI in software testing could emerge as a response to these changing times and environments. AI could help in developing failsafe applications and to enable greater automation in testing to meet these expanded expectations from testing.

How will AI work in Software Testing?

As we move deeper into the age of digital disruption, the traditional ways of developing and delivering software are inadequate to fuel innovation. Delivery timelines are reducing but the technical complexity is rising. With Continuous Testing gradually becoming the norm, organizations are trying to further accelerate the testing process to bridge the chasm between development, testing, and operations in the DevOps environment.

AI helps organizations achieve this pace of accelerated testing and helps them test smarter and not harder. AI has been called, “A field of study that gives computers the ability to learn without being explicitly programmed”. This being the case, organizations can leverage AI to drive automaton by leveraging both supervised and unsupervised methods.

An AI-powered testing platform can easily recognize changed controls promptly. The constant updates in the algorithms will ensure that even the slightest changes can be identified easily.

AI in test automation can be employed for object application categorizations for all user interfaces very effectively. Upon observing the hierarchy of controls, testers can create AI enabled technical maps that look at the graphical user interface (GUI) and easily obtain the labels for different controls.

AI can also be employed effectively to conduct exploratory testing within the testing suite. Risk preferences can be assigned, monitored, and categorized easily with AI. It can help testers in creating the right heat maps to identify bottlenecks in processes and help in increasing test accuracy.

AI can be leveraged effectively to identify behavioral patterns in application testing, defect analysis, non-functional analytics, analysis data from social media, estimation, and efficiency analysis. Machine Learning, a part of AI, algorithms can be employed to test programs and to generate robust test data and deep insights, making the testing process more in-depth and accurate.

AI can also increase the overall test coverage and the depth and the scope of the tests as well. AI algorithms in software testing can be put to work for test suite optimization, enhancing UI testing, traceability, defect analysis, predicting the next test for queuing, determine pass/fail outcomes for complex and subjective tests, rapid impact analysis etc. Since 80% of all tests are repetitive, AI can free up the tester’s time and helps them focus on the more creative side of testing.

Conclusion:

Perhaps the ultimate objective of using AI in software testing is to aim for a world where the software will be able to test, diagnose, and self-correct. This could enable quality engineering and could further reduce the testing time from days to mere hours. There are signs that the use of AI in software testing can save time, money, and resources and help the testers focus their attention on doing the one thing that matters – release great software.

This is now a software-defined world. Almost every company today is a technology company. Every product, in some way, is a technology product. As businesses lean more heavily on technology and software, the software development and technology landscape become even more dynamic. Technology is in a constant state of flux, with one shiny new object outshining the one from yesterday. The stakeholders of software development, the testers, developers, designers etc. thus need to constantly re-evaluate their skills. In this environment of constant change, here are, in my opinion, the five most in-demand technology skills to possess today, and why?

R:Owing to the advances in machine learning, the R programming language is having its coming of age moment now. This open source language has been a workhorse for sorting and manipulating large data sets and has shown its versatility in model building, statistical operations, and visualizations. R, over the years, has become a foundational tool in expanding AI to unlock large data blocks. As data became more dominant, R has made itself quite comfortable in the data science arena. In fact, this language is predicted to surpass the use of Python in data science as R, in contrast to Python, allows robust statistical models to be written in just a few lines. As the world falls more in love with data science it will also find itself getting closer to R.

React: Amongst client-side technologies, React has been growing in popularity rapidly. While the number of frameworks based on JavaScript continues to increase, React still dominates this space. Open Sourced by Facebook in 2013, React has been climbing up the technology charts owing to its ease of use, high level of flexibility and responsiveness, its virtual DOM (document object model) capabilities, its downward data binding capabilities, the ease of enabling migrations, and light-weightiness. React is also winning in the NPM download race and has won the crown of the Best JavaScript framework of 2018. In the age of automation, React gives developers a framework that allows them to break down complex components and reuse codes to complete projects faster. Its unique syntax that allows HTML quotes, as well as HTML tag syntax, help in promoting construction of machine-readable codes. React also gives developers the flexibility to break down complex UI/UX development into simpler components and allows them to make every component intuitive. It also has excellent runtime performance.

Swift: In 2017 we heard reports of the declining popularity of Swift. One of the main reasons for the same was a perceived preference among developers’ to use multiplatform tools. Swift, that is merely four years old, ranked 16 on the TIOBE index despite having a good start. The reason was mainly the changing methodologies in the mobile development ecosystem. However, in 2018 we seem to be witnessing the rise of Swift once again. According to a study conducted by analyst firm RedMonk, Swift tied with Object C at rank 10 in their January 2018 report. It fell one place in the June report, but that could be attributed to the lack of a server-side presence, something IBM has been working to rectify in keeping with its enterprise push. Once Swift became open source it has grown in popularity and has also matured as a language. With iOS apps proving to be more profitable than Android apps, we can expect more developers to switch to Swift. Swift is also finding its way into business discussions as enterprises look at robust iOS apps that offer performance as well as security.

Test Automation:Organizations are racing to achieve business agility. This drive has promoted the rise of new development methodologies and the move towards continuous integration and continuous delivery. In this need for speed Test automation will continue to rise in prominence as it enables faster feedback. The push towards digital transformation in enterprises is also putting the focus on testing and quality assurance. I expect Shift-left testing to grow to hasten software development. Test automation is rapidly emerging as the enabler of software confidence. With the rising interest in new technologies like IoT and blockchain, test automation is expected to get a further push. The possible role of AI in testing is also something to look out for as AI could bring in more intelligence, validation, efficiency, and automation to testing. These could be exciting times for those in the testing and test automation space.

UX:Statistics reveal that 90% of users stop using an application with a bad UX. 86% of users uninstall an app if they encounter problems with its functionality in design. UX or User Experience will continue to rise in prominence as it is the UX that earns users interest and ultimately their loyalty. The business value of UX will rise even further as we delve deeper into the app economy. The role of UX designers is becoming even more compelling as we witness the rise of AR, chatbots and virtual assistants. With the software products and services market becoming increasingly competitive, businesses have to focus heavily on UX design to deliver intuitive and coherent experiences to their users that drive usage and foster adoption.

It is an exciting time for us in the technology game. Innovation, flexibility, simplicity, reliability, and speed have become important contributors to software success. The key differentiator in these dynamic times may be the technology skills that you as an individual or as a technology-focused organization possess. To my mind, the skills that will help you stay ahead are those I’ve identified here.

As the requirement for high-quality software in short time frames and restricted budgets increases, developers are looking for approaches that make building software a lot faster and more efficient. DevOps greatly helps in improving the software product delivery process; by bridging the gap between the development and operations teams, DevOps facilitates greater communication and collaboration, and improves service delivery, while reducing errors and improving quality. According to the State of Agile report, 58% of organizationsembrace DevOps to accelerate delivery speed.

Tools for a successful DevOps Strategy

DevOps creates a stable operating environment and enables rapid software delivery through quick development cycles – all while optimizing resources and costs. However, before you embark on the DevOps journey, it is important to understand that since DevOps integrates people, processes, and tools together, more than tools and technology, it requires a focus on people and organizational change. Begin by driving an enterprise-wide movement – right from the top-level management down to the entry-level staff – and ensure everyone is informed of the value DevOps brings to the organization before integrating them together into cross-functional teams.

Next, selecting the right tools is critical to the success of your DevOps strategy; make sure the tools you select work with the cloud, support network, and IT resources and comply with the necessary security and governance requirements. Here’s your 5-point guide for developing a successful DevOps strategy and the tools you would need to drive sufficient value:

Understand your Requirements: Although this would seem a logical first step, many organizations often make the DevOps plunge in haste, without sufficient planning. Start by understanding the solution patterns of the applications you plan to build. Consider all important aspects of software development including security, performance, testing, and monitoring — basically all of the core details. Use tools like Pencil, a robust prototyping platform, to gather requirements and create mockups. With hundreds of built-in shape collections, you can simplify drawing operations and enable easy GUI prototyping.

Define your DevOps Process: Implementing a DevOps strategy might be the ideal thing to do, but understanding what processes you want to employ and what end result you are looking to achieve is equally important. Since DevOps processes differ from organization to organization, it is important to understand which traditional approaches to development and operations to let go of as you move to DevOps. Tools like GitHub can enable you to improve development efficiency and enjoy flexible deployment options, centralized permissions, innumerable integrations and more. GitHub allows you to host and review code, manage projects, and build quality software – moving ideas forward and learning all along the way.

Fuel Collaboration: Collaboration is a key element of any DevOps strategy. It is only through continuous collaboration that you can develop and review code and stay abreast with all the happenings. With frequent and efficient collaboration, you can efficiently share workloads, enable frequent reviews, be informed of every update, resolve simple conflicts with ease, and improve the quality of your code. Collaboration tools like Jira and Asana enable you to plan and manage tasks with your team across the software development lifecycle. While Jira allows team members to effectively plan and distribute tasks, prioritize and discuss team’s work, and build and release great software together, Asana allows project leaders to assign responsibilities throughout the project; you can prioritize tasks, assign timelines, view individual dashboards and communicate on project goals.

Enable Automated Testing: When developing a DevOps strategy, it is important to enable automated testing. Automated test scripts speed up the process of testing, and also improve the quality of your software by testing it thoroughly at each stage. By leveraging real-world data, they reflect production-level loads and identify issues in time. DevOps-friendly tools like Selenium are ideal for enabling automated testing. Since Selenium supports multiple operating systems and browsers, you can write test scripts in various languages including Java, Python, Ruby and more and can also extend test capability using additional test libraries.

Continuously Monitor Performance: To get the most out of your DevOps strategy, measuring and monitoring performance is key. Given the fact that there will be hundreds of services and processes running in your DevOps environment, all of which cannot be monitored, the identification of the key metrics you want to track is vital. Tools like Jenkins can be used to continuously monitor your development cycles, deployment accuracy, system vulnerabilities, server health, and application performance. By quickly identifying problems, it enables you to integrate project changes more easily and deliver a functional product more quickly.

Improve Service Delivery

Implementing a DevOps strategy is not just about building high-quality software faster; it’s about driving a cultural shift across the organization to improve development processes and make it more efficient. Making the most of a switch to DevOps requires you to start with a new outlook, along with the use of new tools and new processes. By using the right tools at every stage, you can accelerate the product development process, meet time-to-market deadlines, and begin your journey towards improved service delivery and optimized costs.

Let’s dive into Top 90 QA Interview Questions answers that we will recommend you while appearing for any QA interview.

What is Software Quality Assurance (SQA)?

Software quality assurance is an umbrella term, consisting of various planned process and activities to monitor and control the standard of whole software development process so as to ensure quality attribute in the final software product.

What is Software Quality Control (SQC)?

With the purpose similar to software quality assurance, software quality control focuses on the software instead to its development process to achieve and maintain the quality aspect in the software product.

What is Software Testing?

Software testing may be seen as a sub-category of software quality control, which is used to remove defects and flaws present in the software, and subsequently improves and enhances the product quality.

No, but the end purpose of all is same i.e. ensuring and maintaining the software quality.

Then, what’s the difference between SQA, SQC and Testing?

SQA is a broader term encompassing both SQC and testing in it and ensures software development process quality and standard and subsequently in the final product also, whereas testing which is used to identify and detect software defects is a sub-set of SQC.

Software testing life cycle defines and describes the multiple phases which are executed in a sequential order to carry out the testing of a software product. The phases of STLC are requirement, planning, analysis, design, implementation, execution, conclusion and closure.

How STLC is related to or different from SDLC (software development life cycle)?

Both SDLC and STLC depict the phases to be carried out in a subsequent manner, but for different purpose. SDLC defines each and every phase of software development including testing, whereas STLC outlines the phases to be executed during a testing process. It may be inferred that STLC is incorporated in the SDLC phase of testing.

Entry and exit criteria is defined and specified to initiate and terminate a particular testing process or activity respectively, when certain conditions, factors and requirements is/are being met or fulfilled.

What do you mean by the requirement study and analysis?

Requirement study and analysis is the process of studying and analysing the testable requirements and specifications through the combined efforts of QA team, business analyst, client and stakeholders.

What are the different types of requirements required in software testing?

SRS layouts the functional and non-functional requirements for the software to be developed whereas BRS reflects the business requirement i.e., the business demand of a software product as stated by the client.

Why there is a bug/defect in software?

A bug or a defect in software occurs due to various reasons and conditions such as misunderstanding or requirements, time restriction, lack of experience, faulty third party tools, dynamic or last time changes, etc.

What is a software testing artifact?

Software testing artifact or testing artifact are the documents or tangible products generated throughout the testing process for the purpose of testing or correspondence amongst the team and with the client.

What are test plan, test suite and test case?

Test plan defines the comprehensive approach to perform testing of the system and not for the single testing process or activity. A test case is based on the specified requirements & specifications define the sequence of activities to verify and validate one or more than one functionality of the system. Test suite is a collection of similar types of test cases.

How to design test cases?

Broadly, there are three different approaches or techniques to design test cases. These are

Black box design technique, based on requirements and specifications.

White box design technique based on internal structure of the software application.

Experience based design technique based on the experience gained by a tester.

What is test environment?

A test environment comprises of necessary software and hardware along with the network configuration and settings to simulate intended environment for the execution of tests on the software.

Why test environment is needed?

Dynamic testing of the software requires specific and controlled environment comprising of hardware, software and multiple factors under which a software is intended to perform its functioning. Thus, test environment provides the platform to test the functionalities of software in the specified environment and conditions.

What is test execution?

Test execution is one of the phases of testing life cycle which concerns with the execution of test cases or test plans on the software product to ensure its quality with respect to specified requirements and specifications.

What are the different levels of testing?

Generally, there are four levels of testing viz. unit testing, integration testing, system testing and acceptance testing.

What is unit testing?

Unit testing involves the testing of each smallest testable unit of the system, independently.

What is the role of developer in unit testing?

As developers are well versed with their lines of code, they are preferred and being assigned the responsibility of writing and executing the unit tests.

What is integration testing?

Integration testing is a testing technique to ensure proper interfacing and interaction among the integrated modules or units after the integration process.

What are stubs and drivers and how these are different to each other?

Stubs and drivers are the replicas of modules which are either not available or have not been created yet and thus they works as the substitutes in the process of integration testing with the difference that stubs are used in top bottom approach and drivers are used in bottom up approach.

What is system testing?

System testing is used to test the completely integrated system as a one system against the specified requirements and specifications.

What is acceptance testing?

Acceptance testing is used to ensure the readiness of a software product with respect to specified requirement and specification in order to get readily accepted by the targeted users.

Different types of acceptance testing.

Broadly, acceptance testing is of two types-alpha testing and beta testing. Further, acceptance testing can also be classified into following forms:

Both alpha and beta testing are the forms of acceptance testing where former is carried out at development site by the QA/testing team and the latter one is executed at client site by the intended users.

What are the different approaches to perform software testing?

Generally, there are two approaches to perform software testing viz. Manual testing and Automation. Manual testing involves the execution of test cases on the software manually by the tester whereas automation process involves the usage of automation framework and tools to automate the task of test scripts execution.

What is the advantage of automation over manual testing approach and vice-versa?

Is there any testing technique that does not needs any sort of requirements or planning?

Yes, but with the help of test strategy using check lists, user scenarios and matrices.

Difference between ad-hoc testing and exploratory testing?

Both ad-hoc testing and exploratory testing are the informal ways of testing the system without having proper planning & strategy. However, in ad-hoc testing, a tester is well-versed with the software and its features and thereby carries out the testing whereas in exploratory, he/she gets to learn and explore more about the software during the course of testing and thus tests the system gradually along with software understanding and learning throughout the testing process.

How monkey testing is different from ad-hoc testing?

Both monkey and ad-hoc testing are the informal approach of testing but in monkey testing, a tester does not requires the pre-understanding and detailing of the software, but learns about the product during the course of testing whereas in ad-hoc testing, tester has the knowledge and understanding of the software.

Why non-functional testing is equally important to functional testing?

Functional testing tests the system’s functionalities and features as specified prior to software development process. It only validates the intended functioning of the software against the specified requirement and specification but the performance of the system to function in the unexpected circumstances and conditions in real world environment at the users end and to meet customer satisfaction is done through non-functional testing technique. Thus, non-functional testing looks after the non-functional traits of the software.

Which is a better testing methodology: black-box testing or white-box testing?

Both black-box and white-box testing approach have their own advantages and disadvantages. Black-box testing approach enables testers to externally test the system on the basis of specified requirement and specification and does not provide the scope of testing the internal structure of the system, whereas white-box testing methodology verify and validates the software quality through testing of its internal structure and working.

If black-box and white-box, then why gray box testing?

Gray box testing is a third type of testing and a hybrid form of black-box and white-box testing approach, which provides the scope of externally testing the system using test plans and test cases derived from the knowledge and understanding of internal structure of the system.

Difference between static and dynamic testing of software.

The primary difference between static and dynamic testing approach is that the former does not involves the execution of code to test the system whereas latter approach requires the code execution to verify and validate the system quality.

Smoke and Sanity testing are used to test software builds. Are they similar??

Although, both smoke and sanity testing is used to test software builds but smoke testing is used to test the initial build which are unstable whereas sanity tests are executed on relatively stable builds which had undergone multiple time through regression testing.

When, what and why to automate?

Automation is preferred when the execution of tests needs to be carried out repetitively for a longer period of time and within the specified deadlines. Further, an analysis of ROI on automation is desired to analyse the cost-benefit model of the automation. Preferably functional, regression and functional tests may be automated. Further, tests which requires accuracy and precision, and is time-consuming may be considered for automation, including data driven tests also.

What are the challenges faced in automation?

Some of the common challenges faced in the automation are

Initial cost is very high along with the maintenance costs. Thus, requires proper analysis to assess ROI on automation.

Increased complexities.

Limited time.

Demands skilled tester, having appropriate knowledge of programming.

Automation training cost and time.

Selection of right and appropriate tools and frameworks.

Less flexible.

Keeping test plans and cases updated and maintained.

Difference between retesting and regression testing.

Both retesting and regression testing is done after modification in software features and configuration to remove or correct the defect(s). However, retesting is done to validate that the identified defects has been removed or resolved after applying patches while regression testing is done to ensure that the modification in the software doesn’t impacts or affects the existing functionalities and originality of the software.

How to categorize bugs or defects found in the software?

A bug or a defect may be categorized on the priority and severity basis, where priority defines the need to correct or remove defect, from business perspective, whereas severity states the need to resolve or eliminate defect from software requirement and quality perspective.

What is the importance of test data?

Test data is used to drive the testing process, where diverse types of test data as inputs are provided to the system to test the response, behaviour and output of the system, which may be desirable or unexpected.

Why agile testing approach is preferred over traditional way of testing?

Agile testing follows the agile model of development, which requires no or less documentation and provides the scope of considering and implementing the dynamic and changing requirements along with the direct involvement of client or customer to work on their regular feedbacks and requirements to provide software in multiple and short iterative cycles.

What are the parameters to evaluate and assess the performance of the software?

Parameters which are used to evaluate and assess the performance of the software are active defects, authored tests, automated tests, requirement coverage, no. of defects fixed/day, tests passed, rejected defects, severe defects, reviewed requirements, test executed and many more.

How important is the localization and globalization testing of a software application?

Globalization and localization testing ensures the software product features and standards to be globally accepted by the world wide users and to meet the need and requirements of the users belonging to a particular culture, area, region, country or locale, respectively.

Verification is done throughout the development phase on the software under development whereas validation is performed over final product produced after the development process with respect to specified requirement and specification.

Does test strategy and test plan define the same purpose?

Yes, the end purpose of test strategy and test plan is same i.e. to works as a guide or manual to carry out the software testing process, but still they both differs.

Which is better approach to perform regression testing: manual or automation?

Automation would provide better advantage in comparison to manual for performing regression testing.

What is bug life cycle?

Bug or Defect life cycle describes the whole journey or the life of a defect through various stages or phases, right from when it is identified and till its closure.

No, as one of the principles of software testing states that exhaustive testing is not possible.

Why exploratory testing is preferred and used in the agile methodology?

As agile methodology requires the speedy execution of the processes through small iterative cycles, thereby calls for the quick, and exploratory testing which does not depends on the documentation work and is carried out by tester through gradual understanding of the software, suits best for the agile environment.

Difference between load and stress testing.

The primary purpose of load and stress testing is to test system’s performance, behaviour and response under different varied load. However, stress testing is an extreme or brutal form of load testing where a system under increasing load is subjected to certain unfavourable conditions like cut down in resources, short or limited time period for execution of task and various such things.

What is data driven testing?

As the name specifies, data driven testing is a type of testing, especially used in the automation, where testing is carried out and drive by the defined sets of inputs and their corresponding expected output.

When to start and stop testing?

Basically, on the availability of software build, testing process starts. However, testing may be started early with the development process, as soon as the requirements are gathered and available. Moreover, testing depends upon the requirement of the software development model like in waterfall model, testing is done in the testing phase, whereas in agile testing is carried out in multiple and short iteration cycle.

Testing is an infinite process as it is impossible to make a software 100% bug free. But still, there are certain conditions specified to stop testing such as:

The primary advantage of using the traceability matrix is that it maps the all the specified requirements with that to test cases, thereby ensures complete test coverage.

What is software testability?

Software testability comprises of various artifacts which gives the estimation about the efforts and time required in the execution of a particular testing activity or process.

What is positive and negative testing?

Positive testing is the activity to test the intended and correct functioning of the system on being fed with valid and appropriate input data whereas negative testing evaluates the system’s behaviour and response in the presence of invalid input data.

Cookie is used to store the personal data and information of a user at server location, which is later used for making connections to web pages by the browsers, and thus it is essential to test these cookies.

A QA engineer has multiple roles and is bounded to several responsibilities such as defining quality parameters, describing test strategy, executing test, leading the team, reporting the defects or test results.

What is rapid software testing?

Rapid software testing is a unique approach of testing which strikes out the need of any sort of documentation work, and motivates testers to make use of their thinking ability and vision to carry out and drive the testing process.

Difference between error, defect and failure.

In the software engineering, error defines the mistake done by the programmers. Defect reflects the introduction of bugs at production site and results into deviation in results from its expected output due to programming mistakes. Failure shows the system’s inability to execute functionalities due to presence of defect. i.e. defect explored by the user.

Whether security testing and penetration testing are similar terms?

No, but both testing types ensure the security mechanism of the software. However, penetration testing is a form of security testing which is done with the purpose to attack the system to ensure not only the security features but also its defensive mechanism.

Distinguish between priority and severity.

Priority defines the business need to fix or remove identified defect whereas severity is used to describe the impact of a defect on the functioning of a system.

What is test harness?

Test harness is a term used to collectively define various inputs and resources required in executing the tests, especially the automated tests to monitor and assess the behaviour and output of the system under different varied conditions and factors. Thus, test harness may include test data, software, hardware and many such things.

What constitutes a test report?

A test report may comprise of following elements:

Objective/purpose

Test summary

Logged defects

Exit criteria

Conclusion

Resources used

What are the test closure activities?

Test closure activities are carried out the after the successful delivery or release of the software product. This includes collection of various data, information, testwares pertaining to software testing phase so as to determine and assess the impact of testing on the product.

List out various methodologies or techniques used under static testing.

System testing is done with the perspective to test the system against the specified requirements and specification whereas acceptance testing ensures the readiness of the system to meet the needs and expectations of a user.

Distinguish between use case and test case.

Both use case and test case is used in the software testing. Use case depicts and defines the user scenarios including various possible path taken by the system under different conditions and circumstances to execute a particular task and functionality. On the other side, test case is a document based on the software and business requirements and specification to verify and validate the software functioning.

What is the need of content testing?

In the present era, content plays a major role in creating and maintaining the interest of the users. Further, the quality content attracts the audience, makes them convinced or motivated over certain things, and thus is a productive input for the marketing purpose. Thus, content testing is a must testing to make your software content suitable for your targeted users.

List out different types of documentation/documents used in the software testing.

Test plan.

Test scenario.

Test cases.

Traceability Matrix.

Test Log and Report.

What is test deliverables?

Test deliverables are the end products of a complete software testing process- prior, during and after the testing, which is used to impart testing analysis, details and outcomes to the client.

What is fuzz testing?

Fuzz testing is used to discover coding flaws and security loopholes by subjecting system with the large amount of random data with the intent to break the system.

How testing is different with respect to debugging?

Testing is done with the purpose of identifying and locating the defects by the testing team whereas debugging is done by the developers to fix or correct the defects.

What is the importance of database testing?

Database is an inherited component of a software application as it works as a backend system of the application and stores different types of data and information from multiple sources. Thus, it is crucial to test the database to ensure integrity, validity, accuracy and security of the stored data.

What are the different types of test coverage techniques?

Statement Coverage

Branch Coverage

Decision Coverage

Path Coverage

Why and how to prioritize test cases?

Due to abundance of test cases for the execution within the given testing deadline arises the need to prioritize test cases. Test prioritization involves the reduction in the number of test cases, and selecting & prioritizing only those which are based on some specific criteria.

How to write a test case?

Test cases should be effective enough to cover each and every feature and quality aspect of software and able to provide complete test coverage with respect to specified requirements and specifications.

How to measure the software quality?

There are certain specified parameters, namely software quality metrics which is used to assess the software quality. These are product metrics, process metrics and project metrics.

What are the different types of software quality model?

Mc Call’s Model

Boehm Model

FURPS Model

IEEE Model

SATC’s Model

Ghezzi Model

Capability Maturity Model

Dromey’s quality Model

ISO-9126-1 quality model

What different types of testing may be considered and used for testing the web applications?

Functionality testing

Compatibility testing

Usability testing

Database testing

Performance testing

Accessibility testing

What is pair testing?

Pair testing is a type of ad-hoc testing where pair of testers or tester and developer or tester & user is being formed which are responsible for carrying out the testing of the same software product on the same machine.

Hope these 90 QA Questions has provided you a complete overview of the QA process. We wish above QA interview questions will help you clear your next QA interview. Do share your feedback with us @ [email protected] and let us know how these QA questions have helped you during your QA interview.

Testing your newly-designed code for bugs and malfunction is an important part of the development process. After all, your application or piece of code will be used in different systems, environments, and scenarios after shipping.

According to statistics, 36% of developers claim that they will not implement any new coding techniques or technologies in their work at least for the coming year. This goes to show how fast the turnaround times are in the software development world.

It’s often better to ship a slightly less ambitious but functional product than a groundbreaking, unstable one. However, you can achieve both if you automate your quality assurance processes carefully. Let’s take a look at how and why you should automate your functional tests for a quick and valuable feedback during the coding process.

Benefits of Functional Testing & Automation:

Maintaining your Reputation: Whether you are a part of a large software development company or an independent startup project, your reputation plays a huge role in the public perception of your work. Research shows that 17% of developers agree that unrealistic expectations are the biggest problem in their respective fields. Others state that lack of goal clarity, prioritization, and a lack of estimation also add to the matter. There is always a dissonance between managers and developers, which leads to crunch periods and very quick product delivery despite a lack of QA testing. Automated functional testing of your code can help you maintain a professional image by shipping a working product at the end of the development cycle.

Controlled Testing Environment: One of the best parts of in-house testing is the ability to go above and beyond with how much stress you put on your code. For example, you can strain the application or API with as much incoming data and connections as possible without the fear of the server crashing or some other anomaly. While you can never predict how your code will be used in practice, you can assume as many scenarios as possible and test for those specific scenarios.

Early Bug Detection: Most importantly, functional test automation allows for constant, day-to-day testing of your developed code. You can detect bugs, glitches, and data bottlenecks very quickly in doing so. That way, you will detect problems early in the development stage without relying on test group QA which will or will not come across practical issues. The bugs you discover early on can sometimes steer your development process in an entirely different direction, one that you would be oblivious to without automated, repeated testing.

Is Your Test’s Automation Necessary? Before you decide to design your automated functionality test, it’s important to gauge its necessity in the overall scheme of things. Do you really need an automated test at this moment or can you test your code’s functionality manually for the time being? The reason behind this question is simple – the use of too much automated testing can have adverse effects on the data you collect from it. More importantly, test design takes time and careful scripting, both of which are valuable in the project’s development process. Make sure that you are absolutely sure that you need automated tests at this very moment before you step into the scripting process.

Separate Testing from Checking: Testing and checking are two different things, both of which correlate with what we said previously. In short, when you “check” your code, you will be fully aware, engaged, and present for the process. Testing, on the other hand, is automated and you will only see the end-results as the final data rolls in. Both testing and checking are important in the QA of your project, but they can in no way replace one another. Make sure that both are implemented in equal measure and that you double-check everything that seems off or too good to be true manually.

Map out the Script Fully: Running a partial script through your code won’t bring any tangible results to the table. Worse yet, it will confuse your developers and lead to even more crunch time. Instead, make sure that your script is fully written and mapped out before you put it into automated testing. Make sure that the functional test covers each aspect of your code instead of opting for selective testing. This will ensure that the code is tested for any conflicts and compatibility issues instead of running a step-by-step test.

Multiple Tests with Slight Variations: What you can do instead of opting for several smaller tests is to introduce variations into your functionality test script. Include several variations in terms of scenarios and triggers which your code will go through in each testing phase. This will help you determine which aspects of your project need more polish and which ones are good as they are. Repeated tests with very small variations in between are a great way to vent out any dormant or latent bugs which can rear their head later on. Avoid unnecessary post-launch bug fixes and last-minute changes by introducing a multi-version functionality test early on.

Go for Fast Turnaround: While it is important to check off every aspect of your code in the functional testing phase, it is also important to do so in a timely manner. Don’t rely on overly-complex or long tests in your development process. Even with automation and high-quality data to work with afterward, you will still be left with a lot of analysis and rework to be done as a result. Design your scripts so that they trigger every important element in your code without going into full top-to-bottom testing each time you do so. That way, you will have a fast and reliable QA system available for everyday coding – think of it as your go-to spellcheck option as you write your essay.

Identify & Patch Bottlenecks: Lastly, it’s important to patch out the bottlenecks, bugs, and glitches you receive via the functional test you automated. Once these problems are ironed out, make sure to run your scripts again and check if you were right in your assertion. Running the script repeatedly without any fixes in between runs won’t yield any productive data. As a result, the entire process of functional test automation falls flat due to its inability to course-correct your development autonomously.

In Summation

Once you learn what mistakes are bound to happen again and again, you will also learn to fix them preemptively by yourself without the automated testing script. Use the automation feature as a helpful tool, not as a means to fix your code (which it won’t do by itself).

Patch out your glitches before moving forward and closer to the official launch or delivery of your code to the client. The higher the quality of work you deliver, the better you will be perceived as a professional development firm. It’s also worth noting that you will learn a lot as a coder and developer with each bug that comes your way.

Author: Elisa Abbott is a freelancer whose passion lies in creative writing. She completed a degree in Computer Science and writes about ways to apply machine learning to deal with complex issues. Insights on education, helpful tools, and valuable university experiences – she has got you covered;) When she’s not engaged in assessing translation services for PickWriters you’ll usually find her sipping a cappuccino with a book.

Although Blockchain came into the limelight with the cryptocurrency bitcoin, in the last year or so, companies have become increasingly aware of how Blockchain can bring about transformation across industries. With the cloud storage market expected to grow to $88.91 billion by 2022, the decentralized storage industry is rapidly gaining popularity, and Blockchain will be critical to its success. Since data storage – especially critical financial data – is always vulnerable to security breaches, migrating data from private data centers onto public Blockchains can help enterprises decentralize storage, thereby enhancing availability, scalability, and security of data.

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Current Challenges:

It is not hard to imagine the ever-increasing volume of financial data that is being generated. Data, which will also then have to be managed, stored and analyzed for effective business decision-making. Connected devices, mobile apps, and the increasing need to share data across businesses are all contributing to the increasing demand for storage that is highly available, scalable, and secure.

Businesses that are looking to launch new, data-driven applications face a sea of challenges with respect to time, effort, and management to provision new datasets and databases.

Traditional cloud storage networks are also known to come with latency challenges. Since most of the time, the data that gets stored in a data center will not be in the same location as the business, delays in delivery are the norm – and that doesn’t work well in the financial context where delays of milliseconds can cause huge losses.

What’s more, the need for large databases also necessitates the need for managing large data centers, that require frequent temperature control, periodic updating, and rigorous upkeep -all expensive.

In addition, the road towards a richer, more data-centric way of working is further challenged by a global phenomenon of data breaches from centralized data centers. The outcome is worrisome – the growing storage needs of businesses are driving extraordinarily large volumes of data to be stored in centralized databases.

This creates risk at a scale never seen before. This necessitates the need for de-centralizing data storage, that can not only minimize the risk of a complete shutdown but also ensure efficiency and transparency of data storage.

The Benefits of Decentralized Storage:

As most current cloud-based databases are highly centralized, they are tempting targets for data breaches. Cloud Storage Companies do have several mechanisms in place to avoid the loss of data, such as dispersing duplicate files across various data centers to avoid a breach. That said, decentralizing storage would more or less eliminate the risk and repercussions of disruptions.

Although current networks need to evolve in order to accommodate such decentralized storage infrastructure, the day is not far when data will be supported by a network of decentralized nodes in a more user-friendly and cost-effective manner than the current, central database solutions.

Decentralized storage works by distributing the data across a network of nodes, thereby reducing the strain on a single node or database. Since it utilizes geographically distributed nodes, decentralized storage can avert such catastrophes and ensure the company’s data is always protected. As data is stored across hundreds of individual nodes, intelligently distributed across the globe, no single entity can control access – thus improving security and decreasing costs.

Any attack or outage at a single point will not result in a domino effect, as other nodes in other locations will continue to function without interruption. The distributed nature of these nodes also makes decentralized storage highly scalable, as companies can leverage the power of the network and achieve better up-time.

The Role of Blockchain:

Although one of the biggest achievements of the Internet era has undoubtedly been cloud data storage, it is already under threat of being replaced by Blockchain storage technology. As the need for decentralized storage becomes more and more relevant, the storage industry is looking to make the most of Blockchain’s distributed ledger technology.

Blockchain paves the way for user-centric storage networks, where companies can move data from the current centralized databases to Blockchain data storage, and benefit from a more agile, customizable system. Because storage gets distributed across nodes, companies can enjoy a better speed of retrieval and redundancy by accessing data from the node that is closest to them.

With such attributes that meet the practical demands of storing high volumes of data, Blockchain will partition databases along logical lines that can only be accessed by a decentralized application using a unique key. Such a decentralized network of storage nodes not only reduces latency but also increases the speed by retrieving data in parallel from the nearest and fastest node.

And because there are so many geographically dispersed nodes in a network, the reliability and scalability of decentralized storage are greater. What’s more, since the devices in the nodes aren’t owned or controlled by a single vendor but by several individuals, the availability and reliability of data are improved even further.

The Way Forward:

As industries battle issues of the security and confidentiality of data, the evolution of Blockchain has come like a boon. Touted as a technology with the potential to transform every industry, Blockchain could be particularly beneficial in the data storage game.

By improving business efficiency and bringing transparency in how enterprises store business data, Blockchain is poised to offer myriad benefits such as shared control of data, easy auditing, and secure data exchange. While it may take time for Blockchain to become the default choice for businesses looking to meet their ever-increasing storage needs, it won’t be long before the world opts for this secure, efficient, and scalable solution in an increasingly data-starved world. Are you Blockchain ready?

With 4.57 billion mobile phone users in the world right now, the mobile app development industry is also at its pinnacle. With every company building mobile apps to address external as well as internal customers, there is a pressing need to keep pace with rapidly changing market trends, technology advances, and customer needs. One sure-shot way of out-performing the competition and achieving success is by letting data drive your decisions. Big data can enable you to unearth hidden patterns and customer preferences and you can lean on these to develop state-of-the-art mobile apps. Here’s how big data can play a major role in mobile app development.

Understand Customer Needs: A great mobile app is not one which looks stunning but one which meets the needs of users. Using big data, you can analyze the overwhelming volume of data that users generate on a regular basis and convert it into relevant insights. By understanding how users from different backgrounds, age groups, lifestyles, and geographies relate, react, and interact with mobile apps, you can formulate ideas for new and innovative apps and boost the capabilities of existing ones. Uberuses big data in a big way to improve its customer service; when a customer requests for a cab, Uber analyzes real-time traffic conditions, availability of a driver nearby, estimated time for the journey, etc. and provides a time and cost estimate for improved engagement.

Drive User Experience Analysis: In addition to understanding customer needs, mobile app development also requires you to understand how users use your app. Using big data, you can conduct detailed user experience analysis, get a comprehensive 360-degree view of usage and the user experience, evaluate the engagement for each feature or page, and determine the most sought-after features as well as pain points. You can understand which elements of your mobile app make users spend more time and which cause them to leave. You can then use this information to create a list of the very features that users demand, plan for changes or modifications in the design, improve user experience, and maximize engagement.

Get Access to Real-time Data: Businesses today have to remain in touch with changing trends to stay ahead of the race. Big data helps a great deal in keeping up with the times. By examining real-time data, you can take real-time, data-driven decisions to improve customer satisfaction and bring in higher profit. Using big data, Fitbit tracks real-time health data including sleep, eating, and activity habits to enable better lifestyle choices. The data gathered by Fitbit not only helps individuals become healthier, but it also provides doctors and healthcare practitioners with a clear picture of overall health and habits across a wider population.

Build the Right Marketing Strategies: With a pool of data about user behavior including their likes, dislikes, needs, expectations, and more, you can build the right marketing strategies around how, when and where to target your audience. You can make better decisions of all types, from what type of push notifications to send and what strategy to use in increasing engagement. Using big data, you can analyze users’ demographic data, purchase patterns, and social behavior to modify your marketing messages according to their current interests. By building the right strategies, you can drive adoption, fuel engagement, increase satisfaction and ultimately, grow app revenue.

Enable Personalization: Big data also enables you to optimize search and make it more intuitive and less cumbersome for users. By analyzing data from customer queries, you can prioritize results, deliver better and more contextual experience that matter the most to a particular user. You can also group data and features to provide smarter self-service for immediate answers. Amazon uses big data to enable predictive analysis and offers product suggestions based on a user’s previous purchase history, products they have viewed or liked as well as trending products. By integrating recommendations across the buying cycle – from product discovery until checkout, Amazon delivers the most relevant products and delivers a personalized shopping experience to each shopper.

Drive Revenue:

In a highly mobile world today, the mobile app has become the centerpiece of all communication strategies for every business. It is estimated that the mobile app market will reach $189 billion by 2020. Although thousands of companies across the world are building mobile apps every single day, it is through technologies like big data that you can really boost app-performance and fuel user engagement. Big data puts real-time data to work to offer personalized experiences that cater to the needs of the users in the most effective manner. If the mobile is central to your go-to-market strategy, its time you made the most of big data to build better mobile apps that drive value and revenue.

Nowadays, quality is the driving force behind the popularity as well as the success of a software product, which has drastically increased the requirement to take effective measures for quality assurance. Therefore, to ensure this, software testers are using a defined way of measuring their goals and efficiency, which has been made possible with the use of various software testing metrics and key performance indicators(KPI’s). The metrics and KPIs serve a crucial role and help the team determine the metrics that calculate the effectiveness of the testing teams and help them gauge the quality, efficiency, progress, and the health of the software testing.

Therefore, to help you measure your testing efforts and the testing process, our team of experts have created a list of some critical software testing metrics as well as key performance indicators based on their experience and knowledge.

The Fundamental Software Testing Metrics:

Software testing metrics, which are also known as software test measurement, indicates the extent, amount, dimension, capacity, as well as the rise of various attributes of a software process and tries to improve its effectiveness and efficiency imminently. Software testing metrics are the best way of measuring and monitoring the various testing activities performed by the team of testers during the software testing life cycle. Moreover, it helps convey the result of a prediction related to a combination of data. Hence, the various software testing metrics used by software engineers around the world are:

Derivative Metrics: Derivative metrics help identify the various areas that have issues in the software testing process and allows the team to take effective steps that increase the accuracy of testing.

Defect Density: Another important software testing metrics, defect density helps the team in determining the total number of defects found in a software during a specific period of time- operation or development. The results are then divided by the size of that particular module, which allows the team to decide whether the software is ready for the release or whether it requires more testing. The defect density of a software is counted per thousand lines of the code, which is also known as KLOC. The formula used for this is:

Defect Density = Defect Count/Size of the Release/Module

Defect Leakage: An important metric that needs to be measured by the team of testers is defect leakage. Defect leakage is used by software testers to review the efficiency of the testing process before the product’s user acceptance testing (UAT). If any defects are left undetected by the team and are found by the user, it is known as defect leakage or bug leakage.

Defect Leakage = (Total Number of Defects Found in UAT/ Total Number of Defects Found Before UAT) x 100

Defect Removal Efficiency: Defect removal efficiency (DRE) provides a measure of the development team’s ability to remove various defects from the software, prior to its release or implementation. Calculated during and across test phases, DRE is measured per test type and indicates the efficiency of the numerous defect removal methods adopted by the test team. Also, it is an indirect measurement of the quality as well as the performance of the software. Therefore, the formula for calculating Defect Removal Efficiency is:

DRE = Number of defects resolved by the development team/ (Total number of defects at the moment of measurement)

Defect Category: This is a crucial type of metric evaluated during the process of the software development life cycle (SDLC). Defect category metric offers an insight into the different quality attributes of the software, such as its usability, performance, functionality, stability, reliability, and more. In short, the defect category is an attribute of the defects in relation to the quality attributes of the software product and is measured with the assistance of the following formula:

Defect Category = Defects belonging to a particular category/ Total number of defects.

Defect Severity Index: It is the degree of impact a defect has on the development of an operation or a component of a software application being tested. Defect severity index (DSI) offers an insight into the quality of the product under test and helps gauge the quality of the test team’s efforts. Additionally, with the assistance of this metric, the team can evaluate the degree of negative impact on the quality as well as the performance of the software. Following formula is used to measure the defect severity index.

Review Efficiency: The review efficiency is a metric used to reduce the pre-delivery defects in the software. Review defects can be found in documents as well as in documents. By implementing this metric, one reduces the cost as well as efforts utilized in the process of rectifying or resolving errors. Moreover, it helps to decrease the probability of defect leakage in subsequent stages of testing and validates the test case effectiveness. The formula for calculating review efficiency is:

Review Efficiency (RE) = Total number of review defects / (Total number of review defects + Total number of testing defects) x 100

Test Case Effectiveness: The objective of this metric is to know the efficiency of test cases that are executed by the team of testers during every testing phase. It helps in determining the quality of the test cases.

Test Case Productivity: This metric is used to measure and calculate the number of test cases prepared by the team of testers and the efforts invested by them in the process. It is used to determine the test case design productivity and is used as an input for future measurement and estimation. This is usually measured with the assistance of the following formula:

Test Coverage: Test coverage is another important metric that defines the extent to which the software product’s complete functionality is covered. It indicates the completion of testing activities and can be used as criteria for concluding testing. It can be measured by implementing the following formula:

Test Coverage = Number of detected faults/number of predicted defects.

Another important formula that is used while calculating this metric is:Requirement Coverage = (Number of requirements covered / Total number of requirements) x 100

Test Design Coverage: Similar to test coverage, test design coverage measures the percentage of test cases coverage against the number of requirements. This metric helps evaluate the functional coverage of test case designed and improves the test coverage. This is mainly calculated by the team during the stage of test design and is measured in percentage. The formula used for test design coverage is:

Test Design Coverage = (Total number of requirements mapped to test cases / Total number of requirements) x 100

Test Execution Coverage: It helps us get an idea about the total number of test cases executed as well as the number of test cases left pending. This metric determines the coverage of testing and is measured during test execution, with the assistance of the following formula:

Test Execution Coverage = (Total number of executed test cases or scripts / Total number of test cases or scripts planned to be executed) x 100

Test Tracking & Efficiency: Test efficiency is an important component that needs to be evaluated thoroughly. It is a quality attribute of the testing team that is measured to ensure all testing activities are carried out in an efficient manner. The various metrics that assist in test tracking and efficiency are as follows:

Fixed Defects Percentage: With the assistance of this metric, the team is able to identify the percentage of defects fixed.

(Defect fixed / Total number of defects reported) x 100

Accepted Defects Percentage: The focus here is to define the total number of defects accepted by the development team. These are also measured in percentage.

(Defects accepted as valid / Total defect reported) x 100

Defects Rejected Percentage: Another important metric considered under test track and efficiency is the percentage of defects rejected by the development team.

(Number of defects rejected by the development team / total defects reported) x 100

Defects Deferred Percentage: It determines the percentage of defects deferred by the team for future releases.

(Defects deferred for future releases / Total defects reported) x 100

Critical Defects Percentage: Measures the percentage of critical defects in the software.

(Critical defects / Total defects reported) x 100

Average Time Taken to Rectify Defects: With the assistance of this formula, the team members are able to determine the average time taken by the development and testing team to rectify the defects.

(Total time taken for bug fixes / Number of bugs)

Test Effort Percentage: An important testing metric, test efforts percentage offer an evaluation of what was estimated before the commencement of the testing process vs the actual efforts invested by the team of testers. It helps in understanding any variances in the testing and is extremely helpful in estimating similar projects in the future. Similar to test efficiency, test efforts are also evaluated with the assistance of various metrics:

Number of Test Run Per Time Period: Here, the team measures the number of tests executed in a particular time frame.(Number of test run / Total time)

Test Design Efficiency: The objective of this metric is to evaluate the design efficiency of the executed test.(Number of test run / Total Time)

Bug Find Rate: One of the most important metrics used during the test effort percentage is bug find rate. It measures the number of defects/bugs found by the team during the process of testing.(Total number of defects / Total number of test hours)Number of Bugs Per Test: As suggested by the name, the focus here is to measure the number of defects found during every testing stage.(Total number of defects / Total number of tests)

Average Time to Test a Bug Fix: After evaluating the above metrics, the team finally identifies the time taken to test a bug fix.(Total time between defect fix & retest for all defects / Total number of defects)

Test Effectiveness: A contrast to test efficiency, test effectiveness measures and evaluates the bugs and defect ability as well as the quality of a test set. It finds defects and isolates them from the software product and its deliverables. Moreover, the test effectiveness metrics offer the percentage of the difference between the total number of defects found by the software testing and the number of defects found in the software. This is mainly calculated with the assistance of the following formula:

Test Effectiveness (TEF) = (Total number of defects injected + Total number of defects found / Total number of defect escaped) x 100

Test Economic Metrics: While testing the software product, various components contribute to the cost of testing, like people involved, resources, tools, and infrastructure. Hence, it is vital for the team to evaluate the estimated amount of testing, with the actual expenditure of money during the process of testing. This is achieved by evaluating the following aspects:

Total allocated the cost of testing.

The actual cost of testing.

Variance from the estimated budget.

Variance from the schedule.

Cost per bug fix.

The cost of not testing.

Test Team Metrics: Finally, the test team metrics are defined by the team. This metric is used to understand if the work allocated to various test team members is distributed uniformly and to verify if any team member requires more information or clarification about the test process or the project. This metric is immensely helpful as it promotes knowledge transfer among team members and allows them to share necessary details regarding the project, without pointing or blaming an individual for certain irregularities and defects. Represented in the form of graphs and charts, this is fulfilled with the assistance of the following aspects:

Returned defects are distributed team member vise, along with other important details, like defects reported, accepted, and rejected.

The open defects are distributed to retest per test team member.

Test case allocated to each test team member.

The number of test cases executed by each test team member.

Software Testing Key Performance Indicators(KPIs):

A type of performance measurement, Key Performance Indicators or KPIs, are used by organizations as well as testers to get data that can be measured. KPIs are the detailed specifications that are measured and analyzed by the software testing team to ensure the compliance of the process with the objectives of the business. Moreover, they help the team take any necessary steps, in case the performance of the product does not meet the defined objectives.

In short, Key performance indicators are the important metrics that are calculated by the software testing teams to ensure the project is moving in the right direction and is achieving the target effectively, which was defined during the planning, strategic, and/or budget sessions. The various important KPIs for software testers are:

Active Defects: A simple yet important KPI, active defects help identify the status of a defect- new, open, or fixed -and allows the team to take the necessary steps to rectify it. These are measured based on the threshold set by the team and are tagged for immediate action if they are above the threshold.

Automated Tests: While monitoring and analyzing the key performance indicators, it is important for the test manager to identify the automated tests. Through tricky, it allows the team to track the number of automated tests, which can help catch/detect the critical and high priority defects introduced in the software delivery stream.

Covered Requirements: With the assistance of this key performance indicator the team can track the percentage of requirements covered by at least one test. The test manager monitors the these this KPI every day to ensure 100% test and requirements coverage.

Authored Tests: Another important key performance indicator, authored tests are analyzed by the test manager, as it helps them analyze the test design activity of their business analysts and testing engineers.

Passed Tests: The percentage of passed tests is evaluated/measured by the team by monitoring the execution of every last configuration within a test. This helps the team in understanding how effective the test configurations are in detecting and trapping the defects during the process of testing.

Test Instances Executed: This key performance indicator is related to the velocity of the test execution plan and is used by the team to highlight the percentage of the total instances available in a test set. However, this KPI does not offer an insight into the quality of the build.

Test Executed: Once the test instances are determined the team moves ahead and monitors the different types of test execution, such as manual, automates, etc. Just like test instances executed, this is also a velocity KPI.

Defects Fixed Per Day: By evaluating this KPI the test manager is able to keep a track of the number of defects fixed on a daily basis as well as the efforts invested by the team to rectify these defects and issues. Moreover, it allows them to see the progress of the project as well as the testing activities.

Direct Coverage: This KPI helps to perform a manual or automated coverage of a feature or component and ensures that all features and their functions are completely and thoroughly tested. If a component is not tested during a particular sprint, it will be considered incomplete and will not be moved until it is tested.

Percentage of Critical & Escaped Defects: The percentage of critical and escaped defects is an important KPI that needs the attention of software testers. It ensures that the team and their testing efforts are focused on rectifying the critical issues and defects in the product, which in turn helps them ensure the quality of the entire testing process as well as the product.

Time to Test: The focus of this key performance indicator is to help the software testing team measure the time that a feature takes to move from the stage of “testing” to “done”. It offers assistance in calculating the effectiveness as well as the efficiency of the testers and understanding the complexity of the feature under test.

Defect Resolution Time: Defect resolution time is used to measure the time it takes for the team to find the bugs in the software and to verify and validate the fix. Apart from this, it also keeps a track of the resolution time, while measuring and qualifying the tester’s responsibility and ownership for their bugs. In short, from tracking the bugs and making sure the bugs are fixed the way they were supposed to, to closing out the issue in a reasonable time, this KPI ensures it all.

Successful Sprint Count Ratio: Though a software testing metric, this is also used by software testers as a KPI, once all the successful sprint statistics are collected. It helps them calculate the percentage of successful sprints, with the assistance of the following formula:

Quality Ratio: Based on the passed or failed rates of all the tests executed by the software testers, the quality ratio, is used as both a software testing metrics as well as a KPI. The formula used for this is:

Test Case Quality: A software testing metric and a KPI, test case quality, helps evaluate and score the written test cases according to the defined criteria. It ensures that all the test cases are examined either by producing quality test case scenarios or with the assistance of sampling. Moreover, to ensure the quality of the test cases, certain factors should be considered by the team, such as:

They should be written for finding faults and defects.

Test & requirements coverage should be fully established.

The areas affected by the defects should be identified and mentioned clearly.

Test data should be provided accurately and should cover all the possible situations.

It should also cover success and failure scenarios.

Expected results should be written in a correct and clear format.

Defect Resolution Success Ratio: By calculating this KPI, the team of software testers can find out the number of defects resolved and reopened. If none of the defects are reopened then 100% success is achieved in terms of resolution. Defect resolution success ratio is evaluated with the assistance of the following formula:

Process Adherence & Improvement: This KPI can be used for the software testing team to reward them and their efforts if they come up with any ideas or solutions that simplify the process of testing and make it agile as well as more accurate.

Conclusion:

Software testing metrics and key performance indicators are improving the process of software testing exceptionally. From ensuring the accuracy of the numerous tests performed by the testers to validate the quality of the product, these play a crucial role in the software development lifecycle. Hence, by implementing and executing these software testing metrics and performance indicators you can increase the effectiveness as well as the accuracy of your testing efforts and get exceptional quality.

The ThinkSys growth story is known to a few already. For the longest time, we were known as a QA-focused organization. Over time we added a strong Test Automation thread to that story. Adding new skills and technology areas, the company grew organically and now our several highly-talented engineers provide impeccable service in the field of custom software development, web and mobile app development, Cloud, and a multitude of other software services. As technology continues to become a driver of business transformation, we at ThinkSys strive to meet the end-to-end software development and testing needs of our current client as well as future clients. This meant an expansion of the areas we work in. Here’s what drove out thinking.

The Inclusion of Big Data, IoT, and AI:

For many years, big data, IoT, and AI have been impacting organizations across several industries and applications. Although they have all contributed to businesses in unimaginable ways, it is the convergence of these three powerful technologies that can drive next-generation innovation and transformation: from smart manufacturing to precision surgery, energy automation to smart RFID tags, building automation to smart farming, predictive maintenance systems to chatbots, climate control to intelligent shipment tracking – the things that big data, IoT and AI are helping achieve is incredible! Our customers are also impacted by these technology movements. We started seeing more opportunity to marry these technologies into the solutions we were already providing. It seemed clear, to continue to serve the market we just had to add these three disruptive technologies to our development and testing portfolio to enable our customers to leverage the stunning benefits and experience growth like never before.

Big Data: As technology makes inroads into the business world, the problem of information overload has become rampant. Organizations grappling with massive amounts of data are embracing new strategies such as big data to analyze data and uncover critical insights. According to a report, revenue from big data is expected to reach $210 billion by 2020. We believe that big data has the immense capability in discovering hidden patterns, unknown correlations, customer preferences, and other vital information, enabling organizations to make informed decisions. Our big data services include predictive analytics, data mining, text mining, data optimization, data management, & forecasting that can enable organizations to uncover hidden business opportunities and accelerate business growth. By making smart, data-driven decisions, organizations can identify risks ahead of time and improve operations and risk management.

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IoT: The explosion of IoT has completely transformed the technology world and is bringing the physical and digital aspects of life closer than ever. The total economic value-add for IoT is expected to reach $1.9 trillion by 2020. IoT is enabling businesses to boost operational efficiency and transform their business models. We at ThinkSys are quite certain IoT has the capability to create a world of opportunities; with a more direct integration of the physical world with the digital, IoT will improve business efficiency and accuracy through more intelligent data capture from the edges and more seamless automation. As IoT makes its way into every sector, we aim to cater to the distinct demands of every commercial enterprise and industry. Our end-to-end customized IoT consulting services and implementation solutions can enable organizations to optimize operations, reduce costs, and achieve revenue goals.

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AI: A fundamental shift in business operations is being brought about by AI; according to reports, global spending on AI is expected to reach a whopping $57.6 billion by 2021. Although AI finds great application across industries such as banking, finance, e-commerce, healthcare, and telecommunication, it is reinventing the way goods are manufactured and delivered. The recent proliferation of AI has brought with it a multitude of associated technologies that are enabling organizations to automate processes, improve efficiency and transform businesses. Our foray into AI marks the beginning of our digital journey into advanced AI technologies such as cognitive computing, machine learning, natural language processing, among others. We are already working on solutions that will bring in the required intelligence to improve the speed of processes, reduce errors, and increase accuracy, and precision – thus enabling our clients to be agile, smart and innovative.

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Drive Business Value:

At ThinkSys, we believe technology has the power to fuel business transformation. Leveraging our capabilities and knowledge of the latest tools and applications, we offer time-tested and reliable technology services across a comprehensive portfolio of advanced technologies. Our team of experienced and knowledgeable experts make use of the latest strategies and deliver solutions to solve complex business problems. By expanding our technology portfolio and including big data, IoT, and AI into our service offering, we aim to assist businesses in understanding the information contained within large data sets, to automate critical business processes, and enable them to drive substantial business value in all that they do.

Google’s foray into the cloud computing space is the talk of the town. By offering a suite of public cloud computing services such as compute, storage, networking, big data, IoT, machine learning, and application development, Google has now joined the likes of Amazon and Microsoft and hopes to take over the cloud computing market. Since the platform is a public cloud offering, services can be accessed by application developers, cloud administrators, and other IT professionals over the internet or by using a dedicated network connection.

What Google New Cloud Platform Means For Application Development?

According to Gartner, by 2021, the PaaS market is expected to attain a total market size of $27.3 billion. In addition to the core cloud computing products such as Google Compute Engine, Google Cloud Storage, and Google Container Engine, what’s particularly exciting for the application development world is the Google App Engine – a platform-as-a-service (PaaS) offering that enables developers to build scalable web applications as well as mobile and IoT backends. It offers access to Google’s scalable hosting, software development kit (SDK), and a host of built-in services and APIs. Here’s a list of features application developers can leverage:

Access to familiar languages and tools: Since developers are most comfortable developing apps using languages that they are familiar with, the Google Cloud Platform allows them to choose the language of their choice – from Java, PHP, Node.js, Python, C#,.Net, Ruby or any other language you prefer. Access to a collection of tools and libraries that include Google Cloud SDK, Cloud Shell, Cloud Tools for Android Studio, IntelliJ, PowerShell, Visual Studio etc. make application development all the more efficient. And with custom runtimes, you can bring any library and framework to the App Engine by supplying a Docker container.

Hassle-free Coding: Despite being proficient in coding, developers often end up managing several other aspects of the application development life-cycle beyond the purview of their role. The Google Cloud Platform offers a range of infrastructure capabilities such as patch and server management, as well as security features like firewall, Identity and Access Management, and SSL/ TLS certificates. With all these other facets of development taken care of, developers can enjoy hassle-free coding, without worrying about managing the underlying infrastructure.

Scalable Mobile Backends: Depending on the type of mobile application that is required to be built, the Google Cloud Platform automatically scales the hosting environment. With Cloud Tools for IntelliJ, one can easily deploy Java backends for cloud apps to the Google App Engine flexible environment. Integration with Firebase mobile platform provides an easy-to-use front-end with a scalable and reliable backend, and access to functionalities such as databases, analytics, crash reporting and more.

Quick Deployment: Quick deployment is a top priority for any developer; if one can’t deploy apps quickly, someone else will and might eat into your market share and customer base. Being a fully-managed platform, Google Cloud Platform allows developers to quickly build and deploy applications and scale as required, and not worry about managing servers or configurations. What’s more, Google’s Cloud Deployment Manager allows you to specify all the resources needed for the application and to perform repeatable deployments quickly and efficiently.

High Availability: Making applications available anytime, anywhere, and on any device has become a requisite. The Google App Engine allows developers to build highly scalable applications on a fully managed serverless platform. All they have to do is simply upload their code and allow Google to manage the app’s availability — without having to provision or maintain a single server. Since the engine scales applications automatically in response to the amount of traffic they receive, you can ensure high availability and only pay for the resources used.

Easy Testing: The impact of an app failure is extremely profound. Not only does it cost a lot but it also impacts customer trustworthiness. Do you know? In 2017, software failures resulted in losses of over $1.7 trillion. The Google Cloud Platform integrates with the Firebase Test Lab that provides cloud-based infrastructure for testing mobile apps. With Firebase Test Lab, app developers can initiate the testing of apps across a wide variety of devices and configurations and view test results directly on their console. And if there are problems in the app, they can debug the cloud backend using Stackdriver Debugger without affecting end-user experience.

Seamless Versioning: Users need updated information about the version of the app installed on their devices. This means that versioning is a critical component of the application upgrade and maintenance strategy. When developing apps in the App Engine, one can easily create development, test, staging, and production environments and host different versions of the app. Each version then runs within one or more instances, depending on how much traffic it has been configured to handle.

Health Monitoring: Providing users with high-quality app experiences requires app developers to carry out timely performance monitoring. As applications get more complex and distributed, Google Stackdriver offers powerful application diagnostics to debug and monitor the health and performance of these apps. By aggregating metrics, logs, and events, it offers deep insight into multiple issues. This helps speed up root-cause analysis and reduce mean time to resolution.

Streamline Application Development:

The Google Cloud Platform – with its application development and integration services – could change the face of application development. With access to popular languages and tools and an open and flexible framework that is fully managed, it enables app developers to improve productivity and become more agile. Developers can focus on simply writing code and run all applications in a serverless environment. Since the App Engine automatically scales depending on application traffic and consumes resources only when the code is running, developers do not have to worry about over or under-provisioning. Now developers can efficiently manage resources from the command line, debug source code in their production environment, easily run API backends using industry-leading tools, and streamline the application development process.

With cost savings being a key driver for cloud adoption, many organizations choose the public cloud to achieve economies of scale. Although the public cloud sector continues to attract enterprise customers looking for a combination of price economy and cloud productivity, many customers also look to run several workloads privately within a private cloud. Contrary to popular belief that public cloud platforms are the most economical, recent research suggests that private cloud solutions can be more cost-effective than public cloud infrastructures.

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Why Private Clouds are Becoming Popular Again:

The continuous need for speed and efficiency of operations is making cloud adoption a priority for many businesses today. Cloud services enable modern organizations to break the barriers of traditional business operations and drive innovation at a rapid pace and in affordable ways. According to a study, public cloud adoption increased to 92% and private cloud to 75% in 2018.

Private clouds work better for large enterprises, especially if they operate in regulated industries or have workloads with sensitive data. With private clouds, organizations have more control over their data and enjoy additional security, compliance, and delivery options. Also, with the generational shift in IT management processes and practices, private clouds enable the millennial generation to adopt simplified tools and intuitive graphical user interfaces.

Why Public Clouds Aren’t as Economical as they Seem:

Containing costs is one of the main reasons for public cloud adoption. Other reasons are the access to on-demand resources, quicker time to market, easier product development, and the ability to scale to meet varying needs. However, many organizations do not realize that public clouds are not always the bargain they expect and that they may not deliver the promised cost savings. Although public clouds help organizations grow revenue and increase productivity, with scale, the costs can mount rapidly, without the expected savings accruing to the business.

Also, in order to move workloads to the public cloud, organizations must consider the potentially high cost of re-architecting and re-coding applications. This is significant when compared to the relatively minor premium incurred in maintaining a private cloud. This certainly busts the myth that public clouds are always the cheapest option.

Making Private Clouds Economical:

Although the private cloud has often been touted as the right choice for organizations with mission-critical requirements at a premium price, this is not the full story. There are several ways in which private clouds are more economical than public clouds. 41% of organizations claim to be saving money using a private cloud instead of a public cloud – in addition to the perceived benefits of ownership, control, and security.

For organizations that have the expertise to manage a large number of servers at a high level of utilization, private clouds can offer a total cost of ownership (TCO) advantage.

Organizations that use capacity-planning and budget-management tools can achieve substantial economies of scale. Capacity-planning reduces costs by ensuring the hardware is being utilized with as little waste as possible. And budget-management enables consumption and expenditures to be tracked with the goal of reducing waste and optimizing spending.

High levels of automation an also reduce manual tasks, allowing administrators to devote more time to other critical tasks. Organizations can increase labor efficiency by having access to qualified, experienced engineers. They can reduce operational burdens with the outsourcing and automation of day-to-day operations – high levels of automation drive down management costs significantly.

Another key consideration is how organizations utilize cloud resources. Since TCO of a private cloud is directly proportional to its labor efficiency and utilization, for self-managed private clouds to be cheaper, utilization and labor efficiency must be relatively high. If the infrastructure is only used to about 50% of its capacity, the cloud administrator will need to manage a large portion of the infrastructure to achieve a TCO advantage.

Lower costs can also be achieved by maximizing software license use. If licenses are based on CPUs, organizations can achieve improved license utilization by hosting a large number of virtual machines per CPU in a private cloud as compared to a public cloud where each virtual machine needs to be licensed separately at increased costs.

Choosing What Works Best:

In order to get the most out of their cloud investment, organizations must have a clear understanding of what works best in various cloud scenarios and what does not. They need to get past common myths and public hype around the “public vs. private cloud” debate. Enterprises looking to adopt the private clouds need to deploy it for large projects with high utilization and labor efficiency, using the right license model and the right combination of tools and partnerships to achieve economies of scale.

According to a study, even if the public cloud were to cost half as much as the private cloud, enterprises would migrate only 50% of workloads. This suggests that no matter how economical the public cloud may seem, organizations will still have other compelling reasons to use the private cloud. Organizations can also opt for a multi-cloud strategy to avoid vendor lock-in and leverage the best attributes of each platform. According to a report, 81% of enterprises today have a multi-cloud strategy. We have written previously about the multi-cloud and when it may be right for you. Go ahead, hop across there is that’s the next set of questions in your mind.

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At ThinkSys

We deliver specifically designed services & solutions for our clients, to cater to their unique business goals. We guarantee quality, effectiveness, and quick time to market. Our team uses a personal and innovative approach by evolving ideas and turning them into tangible visual results, which help us deliver top-notch services for Software Development, Software Testing, Blockchain, Big Data, Devops, AI & Cloud.